//+------------------------------------------------------------------+
//|                                              OptimizationSystem.mqh |
//|                                  Copyright 2024, MetaQuotes Software |
//|                                             https://www.mql5.com |
//+------------------------------------------------------------------+
#property copyright "Copyright 2024, MetaQuotes Software"
#property link      "https://www.mql5.com"
#property version   "1.00"
#property strict

// 优化参数结构
struct OptimizationParam {
    string name;           // 参数名称
    double value;          // 参数值
    double min;            // 最小值
    double max;            // 最大值
    double step;           // 步长
};

// 优化结果结构
struct OptimizationResult {
    double fitness;        // 适应度值
    OptimizationParam params[];  // 参数数组
    datetime time;         // 优化时间
};

// 优化系统类
class COptimizationSystem {
private:
    // 优化参数
    int populationSize;    // 种群大小
    int maxIterations;     // 最大迭代次数
    double mutationRate;   // 变异率
    double crossoverRate;  // 交叉率
    double eliteRate;      // 精英率
    
    // 优化结果
    OptimizationResult bestResult;
    OptimizationResult population[];
    
    // 初始化参数
    void InitializeParameters() {
        populationSize = 50;
        maxIterations = 100;
        mutationRate = 0.1;
        crossoverRate = 0.8;
        eliteRate = 0.1;
    }
    
    // 初始化种群
    void InitializePopulation(OptimizationParam &params[]) {
        ArrayResize(population, populationSize);
        ArrayResize(bestResult.params, ArraySize(params));
        
        for(int i = 0; i < populationSize; i++) {
            ArrayResize(population[i].params, ArraySize(params));
            
            for(int j = 0; j < ArraySize(params); j++) {
                population[i].params[j].name = params[j].name;
                population[i].params[j].min = params[j].min;
                population[i].params[j].max = params[j].max;
                population[i].params[j].step = params[j].step;
                
                // 随机生成参数值
                double range = params[j].max - params[j].min;
                int steps = (int)MathRound(range / params[j].step);
                population[i].params[j].value = params[j].min + 
                    MathRand() % steps * params[j].step;
            }
        }
    }
    
    // 评估适应度
    double EvaluateFitness(OptimizationParam &params[]) {
        // 这里需要实现适应度评估逻辑
        // 示例使用简单的随机值
        return MathRand() / 32767.0;
    }
    
    // 选择精英
    void SelectElite() {
        int eliteCount = (int)MathRound(populationSize * eliteRate);
        
        // 按适应度排序
        for(int i = 0; i < populationSize - 1; i++) {
            for(int j = i + 1; j < populationSize; j++) {
                if(population[i].fitness < population[j].fitness) {
                    OptimizationResult temp = population[i];
                    population[i] = population[j];
                    population[j] = temp;
                }
            }
        }
        
        // 保存精英
        for(int i = 0; i < eliteCount; i++) {
            if(population[i].fitness > bestResult.fitness) {
                bestResult = population[i];
            }
        }
    }
    
    // 选择父代
    int SelectParent() {
        // 轮盘赌选择
        double totalFitness = 0;
        for(int i = 0; i < populationSize; i++) {
            totalFitness += population[i].fitness;
        }
        
        double random = MathRand() / 32767.0 * totalFitness;
        double sum = 0;
        
        for(int i = 0; i < populationSize; i++) {
            sum += population[i].fitness;
            if(sum >= random) {
                return i;
            }
        }
        
        return populationSize - 1;
    }
    
    // 交叉操作
    void Crossover(OptimizationParam &parent1[], OptimizationParam &parent2[],
                  OptimizationParam &child1[], OptimizationParam &child2[]) {
        ArrayResize(child1, ArraySize(parent1));
        ArrayResize(child2, ArraySize(parent2));
        
        for(int i = 0; i < ArraySize(parent1); i++) {
            if(MathRand() / 32767.0 < crossoverRate) {
                child1[i] = parent2[i];
                child2[i] = parent1[i];
            } else {
                child1[i] = parent1[i];
                child2[i] = parent2[i];
            }
        }
    }
    
    // 变异操作
    void Mutate(OptimizationParam &params[]) {
        for(int i = 0; i < ArraySize(params); i++) {
            if(MathRand() / 32767.0 < mutationRate) {
                double range = params[i].max - params[i].min;
                int steps = (int)MathRound(range / params[i].step);
                params[i].value = params[i].min + 
                    MathRand() % steps * params[i].step;
            }
        }
    }
    
    // 生成新种群
    void GenerateNewPopulation() {
        OptimizationResult newPopulation[];
        ArrayResize(newPopulation, populationSize);
        
        // 保留精英
        int eliteCount = (int)MathRound(populationSize * eliteRate);
        for(int i = 0; i < eliteCount; i++) {
            newPopulation[i] = population[i];
        }
        
        // 生成新个体
        for(int i = eliteCount; i < populationSize; i += 2) {
            int parent1 = SelectParent();
            int parent2 = SelectParent();
            
            Crossover(population[parent1].params, population[parent2].params,
                     newPopulation[i].params, newPopulation[i+1].params);
            
            Mutate(newPopulation[i].params);
            Mutate(newPopulation[i+1].params);
            
            newPopulation[i].fitness = EvaluateFitness(newPopulation[i].params);
            newPopulation[i+1].fitness = EvaluateFitness(newPopulation[i+1].params);
        }
        
        // 更新种群
        ArrayCopy(population, newPopulation);
    }
    
public:
    // 构造函数
    COptimizationSystem() {
        InitializeParameters();
    }
    
    // 析构函数
    ~COptimizationSystem() {
        ArrayFree(population);
    }
    
    // 设置参数
    void SetParameters(int populationSize, int maxIterations,
                      double mutationRate, double crossoverRate,
                      double eliteRate) {
        this.populationSize = populationSize;
        this.maxIterations = maxIterations;
        this.mutationRate = mutationRate;
        this.crossoverRate = crossoverRate;
        this.eliteRate = eliteRate;
    }
    
    // 运行优化
    void RunOptimization(OptimizationParam &params[]) {
        InitializePopulation(params);
        
        for(int iteration = 0; iteration < maxIterations; iteration++) {
            // 评估适应度
            for(int i = 0; i < populationSize; i++) {
                population[i].fitness = EvaluateFitness(population[i].params);
            }
            
            // 选择精英
            SelectElite();
            
            // 生成新种群
            GenerateNewPopulation();
        }
    }
    
    // 获取最佳结果
    void GetBestResult(OptimizationResult &result) {
        result = bestResult;
    }
    
    // 生成优化报告
    string GenerateReport() {
        string report = "优化报告\n";
        report += "----------------------------------------\n";
        report += "最佳适应度: " + DoubleToString(bestResult.fitness, 4) + "\n";
        report += "优化时间: " + TimeToString(bestResult.time) + "\n";
        report += "最佳参数:\n";
        
        for(int i = 0; i < ArraySize(bestResult.params); i++) {
            report += bestResult.params[i].name + ": " + 
                     DoubleToString(bestResult.params[i].value, 4) + "\n";
        }
        
        return report;
    }
    
    // 清除优化数据
    void ClearData() {
        ArrayFree(population);
        InitializeParameters();
    }
}; 